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Record W3026728563 · doi:10.3386/w26000

Implications of Increasing College Attainment for Aging in General Equilibrium

2019· preprint· en· W3026728563 on OpenAlexafffund
Juan Carlos Conesa, Timothy J. Kehoe, Vegard Nygaard, Gajendran Raveendranathan

Bibliographic record

VenueNational Bureau of Economic Research · 2019
Typepreprint
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsMcMaster University
FundersNational Institutes of HealthMcMaster University
KeywordsEconomicsEarningsProductivityGeneral equilibrium theoryOverlapping generations modelTax rateLabour economicsFertilityEducational attainmentDependency ratioTotal fertility rateDemographic economicsMonetary economicsMacroeconomicsDemographyPopulationEconomic growth

Abstract

fetched live from OpenAlex

We develop an overlapping generations general equilibrium model of the U.S. economy with heterogeneous consumers who face idiosyncratic earnings and health risk to study the implications of increasing college attainment, decreasing fertility, and increasing longevity .While all three trends contribute to a higher old age dependency ratio, increasing college attainment has different implications because it increases labor productivity.Decreasing fertility and increasing longevity require the government to increase the average labor tax rate from 33.5 to 47.1 percent.Increasing college attainment lowers the required tax increase by 12.0 percentage points.The labor tax rate required to balance the government budget is higher under general equilibrium than in a small open economy with a constant interest rate, because the reduction in the interest rate lowers capital income tax revenues.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.463
GPT teacher head0.641
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2019
Admission routes2
Has abstractyes

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